Backtesting Algorithmic Trading Strategy Using Python

Backtesting Crossover Strategy

Backtesting is the process of evaluating a trading strategy using historical data to simulate the performance of the strategy in the past. In this article, we will discuss how to backtest a crossover strategy using Python.

Step 1: Importing Libraries and Loading the Data

Before we can backtest the crossover strategy, we need to import the necessary libraries and load the data. We will use the pandas library to load the historical data and the backtrader library to create and run the backtest.

# Import necessary Libraries

import pandas as pd

import backtrader as bt

# Load the data

data = bt.feeds.YahooFinanceData(dataname='AAPL',

                                 fromdate=datetime(2019, 1, 1),

                                 todate=datetime(2021, 1, 1))

# Create an instance of the CrossoverStrategy

crossover_strategy = CrossoverStrategy()

# Create an instance of the cerebro engine

cerebro = bt.Cerebro()

# Add the data to cerebro

cerebro.adddata(data)

# Add the strategy to cerebro

cerebro.addstrategy(crossover_strategy)

The YahooFinanceData class in backtrader is used to load historical data from Yahoo Finance. We pass the ticker symbol, the start date, and the end date of the data that we want to load. We then create an instance of the CrossoverStrategy class that we defined in the previous article. We also create an instance of the cerebro engine and add the data and the strategy to it.

Step 2: Defining the Strategy Performance Metrics

Before we run the backtest, we need to define the performance metrics that we want to track. We can define the performance metrics using the cerebro.addanalyzer() method. In this example, we will track the total return, the Sharpe ratio, and the maximum drawdown.

# Add the performance metrics

cerebro.addanalyzer(bt.analyzers.Returns)

cerebro.addanalyzer(bt.analyzers.SharpeRatio)

cerebro.addanalyzer(bt.analyzers.DrawDown)

Step 3: Running the Backtest

Next, we need to run the backtest using the run() method of the cerebro engine. This will generate the results of the backtest that we can evaluate.

# Run the backtest

backtest_results = cerebro.run()

Step 4: Retrieving the Strategy Performance Metrics

After running the backtest, we can retrieve the performance metrics using the get_analysis() method of the cerebro engine. We can then print the performance metrics to the console.

# Retrieve the performance metrics

total_return = backtest_results[0].analyzers.returns.get_analysis()['rtot']

sharpe_ratio = backtest_results[0].analyzers.sharperatio.get_analysis()['sharperatio']

max_drawdown = backtest_results[0].analyzers.drawdown.get_analysis()['max']['drawdown']

# Print the performance metrics

print(f"Total return: {total_return:.2%}")

print(f"Sharpe ratio: {sharpe_ratio:.2f}")

print(f"Maximum drawdown: {max_drawdown:.2%}")

Step 5: Visualizing the Backtest Results

Finally, we can visualize the backtest results using the backtrader.plot module. We can use the plot() function to generate a chart that shows the performance of the strategy over time.

# Visualize the backtest results

bt.plot.plot(backtest_results, include_title=False)

The `plot()` function takes the backtest results and generates a chart that shows the performance of the strategy over time. We can use the `include_title` parameter to hide the title of the chart.

Complete Code

Here is the complete code for backtesting a crossover strategy using Python and `backtrader`:

import pandas as pd

import backtrader as bt

from datetime import datetime

class CrossoverStrategy(bt.Strategy):

    def __init__(self):

        self.sma50 = bt.indicators.SimpleMovingAverage(

            self.data.close, period=50)

        self.sma200 = bt.indicators.SimpleMovingAverage(

            self.data.close, period=200)

        self.crossover = bt.indicators.CrossOver(

            self.sma50, self.sma200)

    def next(self):

        if self.crossover > 0:

            self.buy()

        elif self.crossover < 0:

            self.sell()

data = bt.feeds.YahooFinanceData(dataname='AAPL',

                                 fromdate=datetime(2019, 1, 1),

                                 todate=datetime(2021, 1, 1))

crossover_strategy = CrossoverStrategy()

cerebro = bt.Cerebro()

cerebro.adddata(data)

cerebro.addstrategy(crossover_strategy)

cerebro.addanalyzer(bt.analyzers.Returns)

cerebro.addanalyzer(bt.analyzers.SharpeRatio)

cerebro.addanalyzer(bt.analyzers.DrawDown)

backtest_results = cerebro.run()

total_return = backtest_results[0].analyzers.returns.get_analysis()['rtot']

sharpe_ratio = backtest_results[0].analyzers.sharperatio.get_analysis()['sharperatio']

max_drawdown = backtest_results[0].analyzers.drawdown.get_analysis()['max']['drawdown']

print(f"Total return: {total_return:.2%}")

print(f"Sharpe ratio: {sharpe_ratio:.2f}")

print(f"Maximum drawdown: {max_drawdown:.2%}")

bt.plot.plot(backtest_results, include_title=False)

Conclusion

In this article, we discussed how to backtest a crossover strategy using Python and backtrader. We covered how to import the necessary libraries and load the data, how to define the performance metrics, how to run the backtest, how to retrieve the performance metrics, and how to visualize the backtest results. Backtesting is an essential part of developing and evaluating trading strategies, and Python makes it easy to implement and test different strategies.